Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	139,192
2	Rhode Island	137,212
3	South Dakota	137,182
4	Utah	122,689
5	Tennessee	120,410
6	Arizona	117,487
7	Iowa	114,349
8	Oklahoma	112,642
9	Nebraska	112,234
10	Wisconsin	112,168
11	South Carolina	110,824
12	New Jersey	110,769
13	Arkansas	110,580
14	Alabama	106,860
15	Kansas	105,889
16	Indiana	105,872
17	Delaware	104,447
18	Mississippi	104,022
19	Idaho	103,842
20	Illinois	103,408
21	New York	103,153
22	Florida	101,443
23	Nevada	101,070
24	Montana	100,575
25	Georgia	100,061
26	Wyoming	99,274
27	Minnesota	99,107
28	Kentucky	98,838
29	Texas	98,578
30	Massachusetts	97,955
31	Louisiana	97,597
32	Missouri	96,096
33	California	94,230
34	New Mexico	93,290
35	Connecticut	93,159
36	North Carolina	91,242
37	Ohio	90,392
38	Alaska	90,143
39	Michigan	89,008
40	Pennsylvania	87,450
41	Colorado	86,097
42	West Virginia	83,398
43	Virginia	75,958
44	Maryland	72,410
45	New Hampshire	67,700
46	District of Columbia	66,410
47	Washington	51,269
48	Puerto Rico	49,395
49	Maine	43,121
50	Oregon	41,767
51	Vermont	35,436
52	Hawaii	22,471

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Michigan	530
2	Rhode Island	380
3	Puerto Rico	372
4	Delaware	318
5	New Jersey	306
6	Tennessee	296
7	Connecticut	287
8	Pennsylvania	286
9	Colorado	271
10	New York	264
11	Florida	259
12	Maine	254
13	Minnesota	254
14	Alaska	252
15	North Carolina	218
16	Illinois	193
17	Massachusetts	189
18	New Hampshire	189
19	West Virginia	175
20	South Carolina	152
21	North Dakota	151
22	Maryland	150
23	Nebraska	139
24	Virginia	137
25	District of Columbia	136
26	Washington	135
27	Oregon	131
28	Vermont	129
29	Ohio	128
30	Idaho	127
31	Louisiana	125
32	Alabama	124
33	New Mexico	118
34	Indiana	116
35	Nevada	116
36	South Dakota	114
37	Wisconsin	111
38	Montana	109
39	Wyoming	108
40	Iowa	107
41	Texas	103
42	Georgia	99
43	Arizona	96
44	Kentucky	86
45	Utah	79
46	Missouri	76
47	Oklahoma	72
48	Mississippi	62
49	California	56
50	Hawaii	49
51	Kansas	42
52	Arkansas	33

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,837
2	New York	2,631
3	Massachusetts	2,536
4	Rhode Island	2,505
5	Mississippi	2,406
6	Arizona	2,362
7	Connecticut	2,249
8	Louisiana	2,216
9	South Dakota	2,207
10	Alabama	2,202
11	Pennsylvania	2,016
12	North Dakota	1,985
13	Indiana	1,965
14	New Mexico	1,913
15	Illinois	1,893
16	Arkansas	1,890
17	Iowa	1,868
18	South Carolina	1,816
19	Michigan	1,809
20	Georgia	1,806
21	Tennessee	1,757
22	Nevada	1,745
23	Texas	1,717
24	Kansas	1,715
25	Oklahoma	1,692
26	Delaware	1,646
27	Ohio	1,628
28	Florida	1,607
29	West Virginia	1,556
30	District of Columbia	1,554
31	California	1,545
32	Missouri	1,491
33	Montana	1,452
34	Kentucky	1,444
35	Maryland	1,421
36	Wisconsin	1,275
37	Minnesota	1,260
38	Virginia	1,244
39	Wyoming	1,218
40	Nebraska	1,214
41	North Carolina	1,188
42	Idaho	1,132
43	Colorado	1,101
44	New Hampshire	934
45	Washington	715
46	Puerto Rico	692
47	Utah	678
48	Oregon	586
49	Maine	570
50	Alaska	434
51	Vermont	387
52	Hawaii	332

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Kansas	5
2	Michigan	5
3	Connecticut	2
4	Florida	2
5	Kentucky	2
6	Maryland	2
7	Missouri	2
8	Nebraska	2
9	New Jersey	2
10	New York	2
11	Pennsylvania	2
12	Rhode Island	2
13	South Carolina	2
14	Tennessee	2
15	Virginia	2
16	Arizona	1
17	Arkansas	1
18	California	1
19	Colorado	1
20	Georgia	1
21	Illinois	1
22	Iowa	1
23	Louisiana	1
24	Massachusetts	1
25	Minnesota	1
26	Montana	1
27	Nevada	1
28	New Hampshire	1
29	New Mexico	1
30	North Carolina	1
31	North Dakota	1
32	Ohio	1
33	Puerto Rico	1
34	Utah	1
35	Washington	1
36	West Virginia	1
37	Wyoming	1
38	Alabama	0
39	Alaska	0
40	Delaware	0
41	District of Columbia	0
42	Hawaii	0
43	Idaho	0
44	Indiana	0
45	Maine	0
46	Mississippi	0
47	Oklahoma	0
48	Oregon	0
49	South Dakota	0
50	Texas	0
51	Vermont	0
52	Wisconsin	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	355,882	1	99
Chattahoochee	Georgia	349,592	2	99
Bent	Colorado	266,631	3	99
Dewey	South Dakota	245,757	4	99
Lincoln	Arkansas	245,547	5	99
Davidson	Tennessee	140,142	187	94
Richland	South Carolina	109,005	1003	68
York	South Carolina	106,517	1105	64
Orange	California	84,761	2120	32
Pierce	Washington	51,502	2892	7

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Foard	Texas	8,658	1	99
Galax city	Virginia	8,035	2	99
Jerauld	South Dakota	7,948	3	99
Emporia city	Virginia	7,856	4	99
Hancock	Georgia	7,686	5	99
Orange	California	1,543	1821	42
Davidson	Tennessee	1,331	2074	33
York	South Carolina	1,317	2093	33
Richland	South Carolina	1,294	2119	32
Pierce	Washington	701	2745	12

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.